A wide range of electronic devices, including mobile wireless communication devices, personal digital assistants (PDAs), laptop computers, desktop computers, digital cameras, digital recording devices, and the like, employ machine vision techniques to provide versatile imaging capabilities. These capabilities may include object recognition processes/techniques which allow these systems to assist users in recognizing landmarks in their present location, identifying friends and strangers, along with a variety of other tasks.
These object recognition processes/techniques may identify one or more objects within an image by reducing an image of the object to a collection of key “features.” Rather than trying to recognize an object from raw image pixel data, these processes/techniques instead recognize an object by comparing these features from a “training” image containing the object, with a new image which may or may not contain the object. If the new image has features which correspond to the features of the “training” image, the process may determine that the same object is present in both images. The processes/techniques may select features in such a manner that objects may be recognized under a variety of different orientations and under varied lighting conditions.
As mobile devices become increasingly compact, there exists a need for more efficient methods for performing feature generation and recognition. Such improved methods will improve the functionality of the various imaging applications which depend upon these recognition processes/techniques.